Activated SUMOylation restricts MHC class I antigen presentation to confer immune evasion in cancer

Activated SUMOylation is a hallmark of cancer. Starting from a targeted screening for SUMO-regulated immune evasion mechanisms, we identified an evolutionarily conserved function of activated SUMOylation, which attenuated the immunogenicity of tumor cells. Activated SUMOylation allowed cancer cells to evade CD8+ T cell–mediated immunosurveillance by suppressing the MHC class I (MHC-I) antigen-processing and presentation machinery (APM). Loss of the MHC-I APM is a frequent cause of resistance to cancer immunotherapies, and the pharmacological inhibition of SUMOylation (SUMOi) resulted in reduced activity of the transcriptional repressor scaffold attachment factor B (SAFB) and induction of the MHC-I APM. Consequently, SUMOi enhanced the presentation of antigens and the susceptibility of tumor cells to CD8+ T cell–mediated killing. Importantly, SUMOi also triggered the activation of CD8+ T cells and thereby drove a feed-forward loop amplifying the specific antitumor immune response. In summary, we showed that activated SUMOylation allowed tumor cells to evade antitumor immunosurveillance, and we have expanded the understanding of SUMOi as a rational therapeutic strategy for enhancing the efficacy of cancer immunotherapies.

detected genes and <10% mitochondrial reads were retained for downstream analyses. To remove outliers, upper filtering thresholds for the number of genes and UMIs were set at >=6,000 and >=40,000 respectively. Furthermore, only genes detected in >= 3 cells were kept.
Using the expression information from hashtag oligos (HTO), samples were demultiplexed and only singlets were included for subsequent analyses. HTO demultiplexing was done using Seurat's HTODemux function with default parameters. Sample quality was additionally assessed by performing a clustering per condition (control/SUMOi). We found that the three samples were distributed equally among all clusters within the respective treatment group, indicating no hashing bias. Using Seurat's FindMarker function, signature genes for each cluster were determined. Cell populations with co-expression of marker genes for distinct cell types were labeled as doublet clusters and removed prior to data integration.

Data integration, clustering, and cell type annotation
After quality assessment and removal of low-quality cells, samples were integrated to account for batch effects. Data integration was conducted using Seurat's standard integration workflow.
In brief, raw RNA counts were log-normalized to account for differences in library size and the top 2,000 variable genes were identified. Then, integration features were selected and integration anchors determined using CCA (canonical correlation analysis). After scaling, a principal component analysis (PCA) was run on the integrated object and the top 30 principal components (PCs) were used for SNN (shared nearest neighbor) graph construction, clustering (based on Louvain algorithm) and UMAP visualization. For cell type identification, Seurat's FindMarker function (with default parameters) was applied to obtain signature genes for each cluster. Together with knowledge-derived gene lists, cell populations were manually annotated (see Main Figure 8A). After cluster assignment, CD4 T cells, CD8 T cells and γδ T cells were separated and reclustered according to the workflow described above using the top 20 PCs (see Figure 8E).

Differential abundance analysis
To detect differentially abundant cell populations DA-seq was applied (5). The analysis was conducted according to the tutorial (on https://klugerlab.github.io/DAseq/articles/tutorial.html) with values for k ranging from 20 to 500 for both the global and the T cell object (see Figure   8D and Figure 8F), respectively. Although DA-seq can identify differentially abundant subpopulations without requiring clustering information, it does not return any statistics. Therefore, to quantify abundance levels, we additionally performed differential abundance testing using mouse-wise pseudobulks (see Boxplots in Figure 8G and 8H). For this analysis the global dataset was used in which T cells annotations were replaced with the more detailed labels identified in Figure 8E (separate T cell clustering). Initially, cell counts for each sample (mouse) and population were determined. The cell type proportions (=frequency) for each cluster and sample were calculated by dividing the respective number of cells by the total number of cells within that cluster. For statistics, design and contrast matrices were constructed using the model.matrix (stats package version 4.1.0) and makeContrasts function (limma package version 3.48.1). Finally, a Negative Binomial Generalized Linear Model was fitted using the glmFit function (edgeR package version 3.34.0) and likelihood ratio tests were conducted using glmLRT (edgeR package).